This application claims the benefit of Japanese Application No. 2001-279881 filed Sep. 14, 2001.
The present invention relates to a failure prediction apparatus and method for predicting a failure of a superconductive magnet for forming a static magnetic field space employed in a magnetic resonance imaging apparatus, and a magnetic resonance imaging system employing such a failure prediction apparatus and method.
In magnetic resonance imaging processing, spins within a subject are excited by an excitation pulse for each TR, and a magnetic resonance signal generated by the excitation is collected in a two-dimensional Fourier space as a spin echo or gradient echo, for example.
The magnetic resonance signal is given different phase encoding for each xe2x80x9cviewxe2x80x9d, and echo data for a plurality of views are collected with positions on a phase axis differentiated in the two-dimensional Fourier space.
By applying a two-dimensional inverse Fourier transformation on the collected echo data for all the views, an image is reconstructed.
A magnetic resonance imaging apparatus for such magnetic resonance imaging processing comprises a magnet system having an internal cavity (bore) for receiving the subject.
The magnet system comprises a main magnetic field magnet for generating a static magnetic field in the bore, gradient coils for forming gradient magnetic fields to give gradients to the strength of the static magnetic field generated by the main magnetic field magnet, and an RF coil for generating a high frequency magnetic field for exciting the spins within the subject in the static magnetic field space formed by the main magnetic field magnet.
The main magnetic field magnet is made by using a superconductive magnet, for example.
For the magnetic resonance imaging apparatus employing the superconductive magnet, the operating condition of the superconductive magnet is estimated to conduct prediction of a potential failure that, upon occurrence, would cause the user significant loss.
The failure prediction is conducted by, for example, a magnet specialist by drawing graphs of detected data on the pressure of a cooling helium gas in the superconductive magnet, the helium level (remaining amount), the temperatures of first and second stages of a refrigerator, and the temperature of a room in which a compressor is placed, and checking these values, trends, relative relationship and variance or masking misinformation etc., to estimate the condition of the magnet.
As described above, the failure prediction of the superconductive magnet is a diagnosis that can be achieved only by a limited number of technicians who have appreciable experience and knowledge, and it is difficult for those less skilled in magnet technology to make the diagnosis.
Moreover, even technicians having experience and knowledge suffer from a disadvantage that it takes much time to obtain a large amount of information as described above to estimate the operating condition of the magnet.
Therefore, its object is to provide a failure prediction apparatus and method that allow easy estimation of the operating condition of a magnet by a technician of any skill level, and allow accurate prediction of a potential failure, and a magnetic resonance imaging system employing such a failure prediction apparatus and method.
The present invention, in its first aspect for achieving the aforementioned object, is a failure prediction apparatus for predicting a failure of a superconductive magnet for forming a static magnetic field space employed in a magnetic resonance imaging apparatus, comprising: a pressure sensor for detecting a pressure of a cooling medium in said superconductive magnet; a level sensor for detecting a level of said cooling medium; a first temperature sensor for detecting a temperature of a predefined portion of a refrigerator; a second temperature sensor for detecting a temperature of a room in which a compressor is placed for cooling the cooling medium from said refrigerator and supplying the cooling medium to said refrigerator; and calculating means for calculating a Mahalanobis distance of magnet data including, as prespecified parameters, the pressure of the cooling medium detected by said pressure sensor, the level of the cooling medium detected by said level sensor, the temperature of the refrigerator detected by said first temperature sensor, and the room temperature detected by said second temperature sensor for determining whether the magnet is normal or not.
In the first aspect of the present invention, said calculating means resolves the pressure of the cooling medium detected by said pressure sensor, the level of the cooling medium detected by said level sensor, the temperature of the refrigerator detected by said first temperature sensor, and the room temperature detected by said second temperature sensor, as the prespecified parameters, into a plurality of elements, normalizes the elements to form magnet data, and calculates the Mahalanobis distance of the normalized magnet data for determining whether the magnet is normal or not.
Moreover, in the first aspect of the present invention, the apparatus comprises: a database storing data obtained by developing magnet data previously sampled from a normally operating magnet including, as prespecified parameters, a pressure of the cooling medium detected by said pressure sensor, a level of the cooling medium detected by said level sensor, a temperature of the refrigerator detected by said first temperature sensor, and a room temperature detected by said second temperature sensor, into a Mahalanobis reference space; and means for determining whether the operating condition of the superconductive magnet is normal or not by comparing the Mahalanobis distance obtained by said calculating means with the stored data in said database.
Furthermore, in the first aspect of the present invention, the apparatus comprises: a database storing data obtained by resolving magnet data previously sampled from a normally operating magnet including, as prespecified parameters, a pressure of the cooling medium detected by said pressure sensor, a level of the cooling medium detected by said level sensor, a temperature of the refrigerator detected by said first temperature sensor, and a room temperature detected by said second temperature sensor, into a plurality of elements, and developing the resolved magnet data comprised of the plurality of elements into a Mahalanobis reference space; and means for determining whether the operating condition of the superconductive magnet is normal or not by comparing the Mahalanobis distance obtained by said calculating means with the stored data in said database.
The present invention, in its second aspect, is a failure prediction apparatus for predicting a failure of a superconductive magnet for forming a static magnetic field space employed in a magnetic resonance imaging apparatus, comprising: a pressure sensor for detecting a pressure of a cooling medium in said superconductive magnet; a level sensor for detecting a level of said cooling medium; first and second temperature sensors for detecting temperatures of a plurality of portions of a refrigerator; a third temperature sensor for detecting a temperature of a room in which a compressor is placed for cooling the cooling medium from said refrigerator and supplying the cooling medium to said refrigerator; and calculating means for calculating a Mahalanobis distance of magnet data including, as prespecified parameters, the pressure of the cooling medium detected by said pressure sensor, the level of the cooling medium detected by said level sensor, the temperatures of the plurality of portions of the refrigerator detected by said first and second temperature sensors, and the room temperature detected by said third temperature sensor for determining whether the magnet is normal or not.
In the second aspect of the present invention, said calculating means resolves the pressure of the cooling medium detected by said pressure sensor, the level of the cooling medium detected by said level sensor, the temperatures of the plurality of portions of the refrigerator detected by said first and second temperature sensors, and the room temperature detected by said third temperature sensor, as the prespecified parameters, into a plurality of elements, normalizes the elements to form magnet data, and calculates the Mahalanobis distance of the normalized magnet data for determining whether the magnet is normal or not.
Moreover, in the second aspect of the present invention, the apparatus comprises: a database storing data obtained by developing magnet data previously sampled from a normally operating magnet including, as prespecified parameters, a pressure of the cooling medium detected by said pressure sensor, a level of the cooling medium detected by said level sensor, temperatures of the plurality of portions of the refrigerator detected by said first and second temperature sensors, and a room temperature detected by said third temperature sensor, into a Mahalanobis reference space; and means for determining whether the operating condition of the superconductive magnet is normal or not by comparing the Mahalanobis distance obtained by said calculating means with the stored data in said database.
Furthermore, in the second aspect of the present invention, the apparatus comprises: a database storing data obtained by resolving magnet data previously sampled from a normally operating magnet including, as prespecified parameters, a pressure of the cooling medium detected by said pressure sensor, a level of the cooling medium detected by said level sensor, temperatures of the plurality of portions of the refrigerator detected by said first and second temperature sensors, and a room temperature detected by said third temperature sensor, into a plurality of elements, and developing the resolved magnet data comprised of the plurality of elements into a Mahalanobis reference space; and means for determining whether the operating condition of the superconductive magnet is normal or not by comparing the Mahalanobis distance obtained by said calculating means with the stored data in said database.
The present invention, in its third aspect, is a failure prediction method of predicting a failure of a superconductive magnet for forming a static magnetic field space employed in a magnetic resonance imaging apparatus, comprising the steps of: detecting a pressure of a cooling medium in said superconductive magnet, a level of said cooling medium, a temperature of a predefined portion of a refrigerator, and a temperature of a room in which a compressor is placed for cooling the cooling medium from said refrigerator and supplying the cooling medium to said refrigerator; and calculating a Mahalanobis distance of magnet data including, as prespecified parameters, the detected pressure of the cooling medium, level of the cooling medium, temperature of the refrigerator, and temperature of the room in which the compressor is placed for determining whether the magnet is normal or not.
In the third aspect of the present invention, said step of calculating a Mahalanobis distance comprises: resolving the pressure of the cooling medium, the level of the cooling medium, the temperature of the predefined portion of the refrigerator, and the temperature of the room in which the compressor is placed, as the prespecified parameters, into a plurality of elements, normalizing the elements to form magnet data, and calculating the Mahalanobis distance of the normalized magnet data for determining whether the magnet is normal or not.
Moreover, in the third aspect of the present invention, the method comprises: comparing data in a database obtained by developing magnet data previously sampled from a normally operating magnet including, as prespecified parameters, a pressure of the cooling medium, a level of the cooling medium, a temperature of the predefined portion of the refrigerator, and a temperature of the room in which the compressor is placed, into a Mahalanobis reference space, with the Mahalanobis distance obtained at said step of calculating a Mahalanobis distance to determine whether the operating condition of the superconductive magnet is normal or not.
Furthermore, in the third aspect of the present invention, the method comprises: comparing data in a database obtained by resolving magnet data previously sampled from a normally operating magnet including, as prespecified parameters, a pressure of the cooling medium, a level of the cooling medium, a temperature of the predefined portion of the refrigerator, and a temperature of the room in which the compressor is placed, into a plurality of elements, and developing the resolved magnet data comprised of the plurality of elements into a Mahalanobis reference space, with the Mahalanobis distance obtained at said step of calculating a Mahalanobis distance to determine whether the operating condition of the superconductive magnet is normal or not.
The present invention, in its fourth aspect, is a failure prediction method of predicting a failure of a superconductive magnet for forming a static magnetic field space employed in a magnetic resonance imaging apparatus, comprising the steps of: detecting a pressure of a cooling medium in said superconductive magnet, a level of said cooling medium, temperatures of a plurality of portions of a refrigerator, and a temperature of a room in which a compressor is placed for cooling the cooling medium from said refrigerator and supplying the cooling medium to said refrigerator; and calculating a Mahalanobis distance of magnet data including, as prespecified parameters, the detected pressure of the cooling medium, level of the cooling medium, temperatures of the plurality of portions of the refrigerator, and temperature of the room in which the compressor is placed for determining whether the magnet is normal or not.
In the fourth aspect of the present invention, said step of calculating a Mahalanobis distance comprises: resolving the pressure of the cooling medium, the level of the cooling medium, the temperatures of the plurality of portions of the refrigerator, and the temperature of the room in which the compressor is placed, as the prespecified parameters, into a plurality of elements, normalizing the elements to form magnet data, and calculating the Mahalanobis distance of the normalized magnet data for determining whether the magnet is normal or not.
Moreover, in the fourth aspect of the present invention, the method comprises: comparing data in a database obtained by developing magnet data previously sampled from a normally operating magnet including, as prespecified parameters, a pressure of the cooling medium, a level of the cooling medium, temperatures of the plurality of portions of the refrigerator, and a temperature of the room in which the compressor is placed, into a Mahalanobis reference space, with the Mahalanobis distance obtained at said step of calculating a Mahalanobis distance to determine whether the operating condition of the superconductive magnet is normal or not.
Furthermore, in the fourth aspect of the present invention, the method comprises: comparing data in a database obtained by resolving magnet data previously sampled from a normally operating magnet including, as prespecified parameters, a pressure of the cooling medium, a level of the cooling medium, temperatures of the predefined portions of the refrigerator, and a temperature of the room in which the compressor is placed, into a plurality of elements, and developing the resolved magnet data comprised of the plurality of elements into a Mahalanobis reference space, with the Mahalanobis distance obtained at said step of calculating the Mahalanobis distance to determine whether the operating condition of the superconductive magnet is normal or not.
The present invention, in its fifth aspect, is a magnetic resonance imaging system employing a superconductive magnet for forming a static magnetic field space, receiving a subject into said static magnetic field, and imaging a region to be examined in the subject using magnetic resonance, comprising a failure prediction apparatus for the superconductive magnet, including: a pressure sensor for detecting a pressure of a cooling medium in said superconductive magnet; a level sensor for detecting a level of said cooling medium; a first temperature sensor for detecting a temperature of a predefined portion of a refrigerator; a second temperature sensor for detecting a temperature of a room in which a compressor is placed for cooling the cooling medium from said refrigerator and supplying the cooling medium to said refrigerator; and calculating means for calculating a Mahalanobis distance of magnet data including, as prespecified parameters, the pressure of the cooling medium detected by said pressure sensor, the level of the cooling medium detected by said level sensor, the temperature of the refrigerator detected by said first temperature sensor, and the room temperature detected by said second temperature sensor for determining whether the magnet is normal or not.
The present invention, in its sixth aspect, is a magnetic resonance imaging system employing a superconductive magnet for forming a static magnetic field space, receiving a subject into said static magnetic field, and imaging a region to be examined in the subject using magnetic resonance, comprising a failure prediction apparatus for the superconductive magnet including: a pressure sensor for detecting a pressure of a cooling medium in said superconductive magnet; a level sensor for detecting a level of said cooling medium; first and second temperature sensors for detecting temperatures of a plurality of portions of a refrigerator; a third temperature sensor for detecting a temperature of a room in which a compressor is placed for cooling the cooling medium from said refrigerator and supplying the cooling medium to said refrigerator; and calculating means for calculating a Mahalanobis distance of magnet data including, as prespecified parameters, the pressure of the cooling medium detected by said pressure sensor, the level of the cooling medium detected by said level sensor, the temperatures of the plurality of portions of the refrigerator detected by said first and second temperature sensors, and the room temperature detected by said third temperature sensor for determining whether the magnet is normal or not.
According to the present invention, the calculating means is supplied with, for example, data on a pressure of a cooling medium detected by a pressure sensor, data on a level (remaining amount) of the cooling medium detected by a level sensor, data on a first temperature of, for example, a first stage of a refrigerator detected by a first temperature sensor, data on a second temperature of a second stage of the refrigerator detected by a second temperature sensor, data on a third temperature (room temperature) of a room in which a compressor is placed detected by a third temperature sensor.
The calculating means calculates a Mahalanobis distance of magnet data including, as prespecified parameters, the supplied pressure data for the cooling medium, level data for the cooling medium, first and second temperature data for the plurality of portions of the refrigerator and room temperature data, for example.
Alternatively, the calculating means resolves the supplied pressure data for the cooling medium, level data for the cooling medium, first and second temperature data for the plurality of portions of the refrigerator and room temperature data, as the prespecified parameters, for example, into a plurality of elements, normalizes the elements to form magnet data, and calculates the Mahalanobis distance of the normalized magnet data.
Then, the calculated Mahalanobis distance is compared with stored data in a database obtained by developing magnet data previously sampled from a normally operating magnet into a Mahalanobis reference space to determine whether the operating condition of the superconductive magnet is normal or not.
Therefore, the present invention allows easy estimation of the operating condition of a magnet by a technician of any skill level.
Consequently, there is provided the advantage that a potential failure can be accurately predicted beforehand.
Further objects and advantages of the present invention will be apparent from the following description of the preferred embodiments of the invention as illustrated in the accompanying drawings.